186
Views
1
CrossRef citations to date
0
Altmetric
Award Papers

Enhancing the performance of signature-based network intrusion detection systems: an engineering approach

&
Pages 209-222 | Received 02 May 2014, Accepted 12 Aug 2014, Published online: 23 Dec 2014

References

  • Scarfone K, Mell P. Guide to intrusion detection and prevention systems (IDPS), NIST Special Publication 800–94, Feb 2007.
  • Roesch M. Snort: lightweight intrusion detection for networks. Proceedings of Usenix LISA Conference; 1999; Seattle, Washington, USA; p. 229–238.
  • Paxson V. Bro: a system for detecting network intruders in real-time. Comput Netw. 1999;31(23–24):2435–2463. doi: 10.1016/S1389-1286(99)00112-7
  • Sommer R, Paxson V. Outside the closed world: on using machine learning for network intrusion detection. Proceedings of IEEE symposium on security and privacy; 2010; Oakland, California, USA; p. 305–316.
  • Axelsson S. The base-rate fallacy and the difficulty of intrusion detection. ACM T Inform Syst Secur. 2000;3(3):186–205. doi: 10.1145/357830.357849
  • Dreger H, Feldmann A, Paxson V, Sommer R. Operational experiences with high-volume network intrusion detection. Proceedings of ACM Conference on Computer and Communications Security; 2004; Washington, DC, USA; p. 2–11.
  • Fisk M, Varghese G. An analysis of fast string matching applied to content-based forwarding and intrusion detection. Technical Report CS2001–0670, University of California, San Diego; 2002.
  • Pietraszek T. Using adaptive alert classification to reduce false positives in intrusion detection. Proceedings of the symposium on recent advances in intrusion detection; 2004; Sophia Antipolis, France; p. 102–124.
  • Meng W, Li W, Kwok LF. EFM: enhancing the performance of signature-based network intrusion detection systems using enhanced filter mechanism. Comput Secur. 2014;43:189–204. doi: 10.1016/j.cose.2014.02.006
  • Sourdis I, Dimopoulos V, Pnevmatikatos D, Vassiliadis S. Packet pre-filtering for network intrusion detection. Proceedings of ACM/IEEE symposium on architectures for networking and communications systems; 2006; San Jose, CA, USA; p. 183–192.
  • Meng Y, Kwok LF. Adaptive blacklist-based packet filter with a statistic-based approach in network intrusion detection. J Netw Comput Appl. 2014;39:83–92. doi: 10.1016/j.jnca.2013.05.009
  • Meng Y, Kwok LF, Li W. Towards designing packet filter with a trust-based approach using bayesian inference in network intrusion detection. Proceedings of the 8th international conference on security and privacy in communication networks; 2012; Padua, Italy; p. 203–221.
  • Boyer RS, Moore JS. A fast string searching algorithm. Commun ACM. 1977;20(10):762–772. doi: 10.1145/359842.359859
  • Horspool R. Practical fast searching in strings. Softw Pract Exp. 1980;10(6):501–506. doi: 10.1002/spe.4380100608
  • Aho AV, Corasick MJ. Efficient string matching: an aid to bibliographic search. Commun ACM. 1975;18(6):333–340. doi: 10.1145/360825.360855
  • Wu S, Manber U. A fast algorithm for multi-pattern searching. Technical Report TR–94–17, Department of Computer Science, University of Arizona; 1994.
  • Meng Y, Li W, Kwok LF. Towards adaptive character frequency-based exclusive signature matching scheme and its applications in distributed intrusion detection. Comput Netw. 2013;57(17):3630–3640. doi: 10.1016/j.comnet.2013.08.009
  • Pietraszek T. Using adaptive alert classification to reduce false positives in intrusion detection. Proceedings of the 7th international symposium on recent advances in intrusion detection; 2004; Sophia Antipolis, France; p. 102–124.
  • Law KH, Kwok LF. IDS false alarm filtering using KNN classifier. Proceedings of the 5th international conference on information security applications; 2005; Jeju Island, Korea; p. 114–121.
  • Alharby A, Imai H. IDS false alarm reduction using continuous and discontinuous patterns. Proceedings of the 3rd international conference on applied cryptography and network security; 2005; New York, NY, USA; p. 192–205.
  • Meng Y, Kwok LF. Adaptive false alarm filter using machine learning in intrusion detection. Proceedings of the 6th international conference on intelligent systems and knowledge engineering; 2011; Shanghai, China; p. 573–584.
  • Meng Y, Li W, Kwok LF. Intelligent alarm filter using knowledge-based alert verification in network intrusion detection. Proceedings of the 20th international symposium on methodologies for intelligent systems; 2012; Macau, China; p. 115–124.
  • Meng Y, Kwok LF. Adaptive non-critical alarm reduction using hash-based contextual signatures in intrusion detection. Comput Commun. 2014;38:50–59. doi: 10.1016/j.comcom.2013.11.001
  • Meng Y, Kwok LF. Towards an information-theoretic approach for measuring intelligent false alarm reduction in intrusion detection. Proceedings of the 12th IEEE international conference on trust, security and privacy in computing and communications; 2013; Melbourne, Australia; p. 241–248.
  • WEKA, Data Mining Software in Java. Available from: http://www.cs.waikato.ac.nz/ml/weka/
  • DARPA intrusion detection evaluation data set. 1999. Available from: http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/1999data.html
  • Wireshark, Network Protocol Analyzer. Available from: http://www.wireshark.org/

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.